Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data

Shilong Li, Zichen Wang, Luciana A. Vieira, Amanda B. Zheutlin, Boshu Ru, Emilio Schadt, Pei Wang, Alan B. Copperman, Joanne L. Stone, Susan J. Gross, Yu Han Kao, Yan Kwan Lau, Siobhan M. Dolan, Eric E. Schadt, Li Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments. We developed a digital phenotyping algorithm to curate 108,557 pregnancies from EMRs across the Mount Sinai Health System, accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset (N = 60,879) to construct predictive models of preeclampsia across three major pregnancy time periods (ante-, intra-, and postpartum). The resulting models predicted preeclampsia with high accuracy across the different pregnancy periods, with areas under the receiver operating characteristic curves (AUC) of 0.92, 0.82, and 0.89 at 37 gestational weeks, intrapartum and postpartum, respectively. We observed comparable performance in two independent patient cohorts. While our machine learning approach identified known risk factors of preeclampsia (such as blood pressure, weight, and maternal age), it also identified other potential risk factors, such as complete blood count related characteristics for the antepartum period. Our model not only has utility for earlier identification of patients at risk for preeclampsia, but given the prediction accuracy exceeds what is currently achieved in clinical practice, our model provides a path for promoting personalized precision therapeutic strategies for patients at risk.

Original languageEnglish
Article number68
Journalnpj Digital Medicine
Volume5
Issue number1
DOIs
StatePublished - Dec 2022

Fingerprint

Dive into the research topics of 'Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data'. Together they form a unique fingerprint.

Cite this